Manner of death and demographic effects on microbial community composition in organs of the human cadaver
Bibliographic record
Abstract
Abstract The microbiome serves important functions in human health, and postmortem, the microbial signatures of colonized organ tissue could be useful in helping to predict the manner of death in cases where this information is not known. We surveyed the microbiota (16S rRNA V4 amplicon sequencing) of 265 organ tissue samples including liver, blood, brain, heart, prostate, spleen and uterus from cadavers in Italy, Finland and the United States with confirmed manners of death comprising either accidental death, natural death, homicide, and suicide. Geographic locality (i.e. nationality) had a strong effect on observed microbial composition. Differing PERMANOVA results between unweighted and weighted UniFrac (nearly inverse results) suggest that specific bacteria may be associated with ethnicity and age, but that these differences are negligible when taking into account the relative abundance of bacterial taxa; weighted UniFrac measures suggest that although taxonomic composition may not vary significantly between different manners of death, PMI, or BMI categories, the relative abundance of specific taxa vary significantly. Various tissues exhibit differential associations with bacteria, and prostate and uterus were substantially different compared to other organs. For example, in Italian cadavers, the bacteria MLE1-12 permeated nearly all tissues, except the prostate and uterus. We identified specific bacterial ASVs as biomarkers of either natural or accidental death and suicide, but not for homicide. While the manner of death may have an impact on microbial associations, further investigation under more controlled conditions will be needed to validate whether these associations are predictive in forensic determinations. Importance The utilization of microbial data in the context of forensic investigations holds great promise for the field of forensic science. Identification of taxa that are associated with postmortem interval (PMI), specific manners of death (MOD), or other traits such as age, sex, ethnicity, and nationality may allow investigators to refine the circumstantial details surrounding the death of an individual. In this study we find nationality (geographic location of cadaver) to be a dominant predictor of cadaver microbiome composition. We also identify a number of cadaver-specific traits to be associated with microbial alpha- and beta diversity, as well as bacterial taxa that are differentially associated with these traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".